TY - JOUR
T1 - Sensing of indoor air quality—characterization of spatial and temporal pollutant evolution through distributed sensing
AU - Coleman, James R.
AU - Meggers, Forrest
N1 - Funding Information:
This work was supported by the Andlinger Center for Energy and the Environment, and the School of Architecture at Princeton University. This study was made possible by funds from Campus as a Lab, which is supported by the Office of the Dean for Research, the Princeton Environmental Institute, the Andlinger Center for Energy and the Environment, the High Meadows Foundation Sustainability Fund, the Office of the Dean of the College and Facilities. This study was funded in part by the US National Science Foundation’s Sustainability Research Network Cooperative Agreement # 1444758. This work builds upon the precedents put forth by Jovan Pantelic, Adam Rysanek, Clayton Miller, Yuzhen Peng and Arno Schlüter. The ongoing support and guidance of Eric Teitelbaum, and the daily assistance of Justin Hinson were invaluable to the timely execution of the project. We would also like to specifically thank Princeton Facilities for their time, accommodation and cooperation throughout the testing and data collection phases of the project.
Publisher Copyright:
© 2018 Coleman and Meggers.
PY - 2018/5/15
Y1 - 2018/5/15
N2 - Discouraged by the high-cost and lack of connectivity of indoor air quality (iAQ) measurement equipment, we built a platform that would allow us to investigate what kinds of iAQ evolution information could be collected by a low-cost, distributed sensor network. Our platform measures a variety of iAQ metrics (CO 2 , HCHO, volatile organic compounds, NO 2 , O 3 , temperature, and relative humidity), can be flexibly powered by batteries or standard 5 W power supplies, and is connected to an infrastructure that supports an arbitrary number of nodes that push data to the cloud and record it in real-time. Some of the sensors used in our nodes generate data in standard units (like ppm or °C), and others provide an analog signal that cannot be directly converted into standard units. To increase the relative precision of measurements taken by different nodes, we placed all 6 pairs of the nodes used in our deployments in the same environment, recorded how they reacted to changing iAQ, and developed calibration functions to synchronize their signals. We deployed the comparatively cross-calibrated nodes to two different buildings on Princeton University's campus; a fabrication shop and an office building. In both buildings, we placed nodes at key positions in the ventilation supply chain, providing us with the ability to monitor where indoor air pollutants were being introduced, and when they tended to be introduced—enabling us to monitor the evolution of pollutants temporally and spatially. We find that the occupied space of the first building's fabrication shop and the second building's open-plan office have higher levels of volatile organic compounds (VOCs) than outside air. This indicates that both buildings' ventilation systems are unable to supply enough fresh air to dilute VOCs generated inside those spaces. In the second building, we also find indications that other parameters are being driven by set-backs and occupancy. These first deployments demonstrate the ability of low-cost distributed iAQ sensor networks to help researchers identify where and when indoor air pollutants are introduced in buildings.
AB - Discouraged by the high-cost and lack of connectivity of indoor air quality (iAQ) measurement equipment, we built a platform that would allow us to investigate what kinds of iAQ evolution information could be collected by a low-cost, distributed sensor network. Our platform measures a variety of iAQ metrics (CO 2 , HCHO, volatile organic compounds, NO 2 , O 3 , temperature, and relative humidity), can be flexibly powered by batteries or standard 5 W power supplies, and is connected to an infrastructure that supports an arbitrary number of nodes that push data to the cloud and record it in real-time. Some of the sensors used in our nodes generate data in standard units (like ppm or °C), and others provide an analog signal that cannot be directly converted into standard units. To increase the relative precision of measurements taken by different nodes, we placed all 6 pairs of the nodes used in our deployments in the same environment, recorded how they reacted to changing iAQ, and developed calibration functions to synchronize their signals. We deployed the comparatively cross-calibrated nodes to two different buildings on Princeton University's campus; a fabrication shop and an office building. In both buildings, we placed nodes at key positions in the ventilation supply chain, providing us with the ability to monitor where indoor air pollutants were being introduced, and when they tended to be introduced—enabling us to monitor the evolution of pollutants temporally and spatially. We find that the occupied space of the first building's fabrication shop and the second building's open-plan office have higher levels of volatile organic compounds (VOCs) than outside air. This indicates that both buildings' ventilation systems are unable to supply enough fresh air to dilute VOCs generated inside those spaces. In the second building, we also find indications that other parameters are being driven by set-backs and occupancy. These first deployments demonstrate the ability of low-cost distributed iAQ sensor networks to help researchers identify where and when indoor air pollutants are introduced in buildings.
KW - Comparative calibration
KW - Distributed sensing
KW - Indoor Air Quality (IAQ)
KW - Internet of Things (IoT)
KW - Microcontrollers
KW - Sensors
KW - Ventilation
KW - Wireless Sensor Networks (WSN)
UR - http://www.scopus.com/inward/record.url?scp=85064610129&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064610129&partnerID=8YFLogxK
U2 - 10.3389/fbuil.2018.00028
DO - 10.3389/fbuil.2018.00028
M3 - Article
AN - SCOPUS:85064610129
SN - 2297-3362
VL - 4
JO - Frontiers in Built Environment
JF - Frontiers in Built Environment
M1 - 28
ER -